{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = {\n",
    "'state': ['ohio', 'califoria', 'nevada'],\n",
    "'year':[2000, 2001, 2002],\n",
    "'pop': [1.5, 1.7, 2.4]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame = DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   pop      state  year\n",
      "0  1.5       ohio  2000\n",
      "1  1.7  califoria  2001\n",
      "2  2.4     nevada  2002\n"
     ]
    }
   ],
   "source": [
    "print(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame1 = DataFrame( data, columns=[' year', 'state', 'pop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         ohio\n",
      "1    califoria\n",
      "2       nevada\n",
      "Name: state, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(frame1['state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H6K+epwhp5M+04TH8Y0k6Ww/m0zuiudWRvJZ26MphTheX8fSi7dz+5kYC/Hz47P7L+f31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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111fb7630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [2014, 2015, 2016,2017]\n",
    "y = [20, 40, 60, 1]\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dRAIgrsLd3Vm8cQcPLdzEB7lFdG59Cj+/ZBDXjOqpWeoiEihxkWihWerbeGhh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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d486710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [10, 20, 30,40, 60]\n",
    "y = [0.2, 0.3, 0.6, 0.7, 0.87]\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
